Generalized Linear Rule Models
Dennis Wei · Sanjeeb Dash · Tian Gao · Oktay Gunluk

Tue Jun 11th 04:35 -- 04:40 PM @ Room 201

This paper considers generalized linear models using rule-based features, also referred to as rule ensembles, for regression and probabilistic classification. Rules facilitate model interpretation while also capturing nonlinear dependences and interactions. Our problem formulation accordingly trades off rule set complexity and prediction accuracy. Column generation is used to optimize over an exponentially large space of rules without pre-generating a large subset of candidates or greedily boosting rules one by one. The column generation subproblem is solved using either integer programming or a heuristic optimizing the same objective. In experiments involving logistic and linear regression, the proposed methods obtain better accuracy-complexity trade-offs than existing rule ensemble algorithms. At one end of the trade-off, the methods are competitive with less interpretable benchmark models.

Author Information

Dennis Wei (IBM Research)
Sanjeeb Dash (IBM Research)
Tian Gao (IBM Research)

Tian is currently a research staff member in IBM T. J. Watson Research Center. His research interests include machine learning, graphical models, causal discovery, reasoning, and applications.

Oktay Gunluk (IBM Research)

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